Unconstrained Gaze Estimation Using Random Forest Regression Voting

In this paper we address the problem of automatic gaze estimation using a depth sensor under unconstrained head pose motion and large user-sensor distances. To achieve robustness, we formulate this problem as a regression problem. To solve the task in hand, we propose to use a regression forest according to their high ability of generalization by handling large training set. We train our trees on an important synthetic training data using a statistical model of the human face with an integrated parametric 3D eyeballs. Unlike previous works relying on learning the mapping function using only RGB cues represented by the eye image appearances, we propose to integrate the depth information around the face to build the input vector. In our experiments, we show that our approach can handle real data scenarios presenting strong head pose changes even though it is trained only on synthetic data, we illustrate also the importance of the depth information on the accuracy of the estimation especially in unconstrained scenarios.